Christopher Musco

Christopher Musco is an Assistant Professor in the Department of Computer Science and Engineering at New York University’s Tandon School of Engineering. His research focuses on the algorithmic foundations of data science and machine learning, integrating theoretical computer science, numerical linear algebra, and optimization. He holds a Ph.D. from MIT and B.S. degrees in Applied Mathematics and Computer Science from Yale University.

Education:

• Ph.D. in Computer Science, MIT
• B.S. in Applied Mathematics & Computer Science, Yale University

Research Interests: Scalable machine learning, numerical linear algebra, randomized algorithms, sketching/streaming methods, and matrix approximation. His work emphasizes efficient processing of large datasets through algorithmic innovation and theoretical analysis.

Publications: Over 50 peer-reviewed articles, including contributions to NeurIPS, SODA, COLT, and ICML, focusing on matrix approximation, spectral methods, and graph algorithms. Recent work includes breakthroughs in hierarchical matrix approximation and provably accurate nearest neighbor search.

Awards & Funding:

• NSF CAREER Award
• Google Research Scholar Award
• Funding from NSF, DOE, and NYU grants

Labs & Groups: Member of NYU’s Theoretical Computer Science Group and Visualization Imaging and Data Analysis Center (VIDA). Active in organizing the CS Theory Seminar and mentoring over 15 Ph.D. students and postdocs.